Download or read book Deep Saliency Detection and Color Sketch Generation written by Guanbin Li and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Deep Saliency Detection and Color Sketch Generation" by Guanbin, Li, 李冠彬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In recent years, with a wide spread of mobile devices with cameras, image has become an important medium for people to record and share their life, and has thus been witnessed a massive increase. Intelligent technique of image analysis and understanding, which focuses on extracting meaningful information from images, is becoming increasingly important. To keep up with its rapid development, the research and industry community has endeavored to develop advanced image analysis algorithms and their accompanying applications. This thesis demonstrates both novel algorithms in image analysis and a practical application system. It consists of two novel deep learning based salient object detection algorithms and a color sketch generation system. For salient object detection, we present two different approaches. The first one formulates saliency detection as a segment-wise regression problem and introduces a neural network architecture to map each segment to a saliency score. The proposed neural network architecture consists of fully connected layers on top of CNNs responsible for feature extraction at three different scales. The second approach is a deep network which consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image while the second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in saliency maps generated from both of the two proposed methods. Experimental results demonstrate that our two deep learning based saliency detection models significantly improve the state of the art. For color sketch generation, we introduce an interactive drawing system, called ColorSketch, for helping novice users generate color sketches from photos. Our system is motivated by the fact that novice users are often capable of tracing object boundaries using pencil strokes, but have difficulties to choose proper colors and brush over an image region in a visually pleasing way. To preserve artistic freedom and expressiveness, our system lets users have full control over pencil strokes for depicting object shapes and geometric details at an appropriate level of abstraction, and automatically augment pencil sketches using color brushes, such as color mapping, brush stroke rendering as well as blank area creation. Experimental and user study results demonstrate that users, especially novice ones, can generate much better color sketches more efficiently with our system than using traditional manual tools. Subjects: Computer drawing Computer vision

Download or read book DEEP SALIENCY DETECTION COLO written by Guanbin Li and published by Open Dissertation Press. This book was released on 2017-01-26 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Deep Saliency Detection and Color Sketch Generation" by Guanbin, Li, 李冠彬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: In recent years, with a wide spread of mobile devices with cameras, image has become an important medium for people to record and share their life, and has thus been witnessed a massive increase. Intelligent technique of image analysis and understanding, which focuses on extracting meaningful information from images, is becoming increasingly important. To keep up with its rapid development, the research and industry community has endeavored to develop advanced image analysis algorithms and their accompanying applications. This thesis demonstrates both novel algorithms in image analysis and a practical application system. It consists of two novel deep learning based salient object detection algorithms and a color sketch generation system. For salient object detection, we present two different approaches. The first one formulates saliency detection as a segment-wise regression problem and introduces a neural network architecture to map each segment to a saliency score. The proposed neural network architecture consists of fully connected layers on top of CNNs responsible for feature extraction at three different scales. The second approach is a deep network which consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image while the second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in saliency maps generated from both of the two proposed methods. Experimental results demonstrate that our two deep learning based saliency detection models significantly improve the state of the art. For color sketch generation, we introduce an interactive drawing system, called ColorSketch, for helping novice users generate color sketches from photos. Our system is motivated by the fact that novice users are often capable of tracing object boundaries using pencil strokes, but have difficulties to choose proper colors and brush over an image region in a visually pleasing way. To preserve artistic freedom and expressiveness, our system lets users have full control over pencil strokes for depicting object shapes and geometric details at an appropriate level of abstraction, and automatically augment pencil sketches using color brushes, such as color mapping, brush stroke rendering as well as blank area creation. Experimental and user study results demonstrate that users, especially novice ones, can generate much better color sketches more efficiently with our system than using traditional manual tools. Subjects: Computer drawing Computer vision

Download or read book Visual Saliency From Pixel Level to Object Level Analysis written by Jianming Zhang and published by Springer. This book was released on 2019-01-21 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc. In this book, the authors present methods for both traditional and emerging saliency computation tasks, ranging from classical low-level tasks like pixel-level saliency detection to object-level tasks such as subitizing and salient object detection. For low-level tasks, the authors focus on pixel-level image processing approaches based on efficient distance transform. For object-level tasks, the authors propose data-driven methods using deep convolutional neural networks. The book includes both empirical and theoretical studies, together with implementation details of the proposed methods. Below are the key features for different types of readers. For computer vision and image processing practitioners: Efficient algorithms based on image distance transforms for two pixel-level saliency tasks; Promising deep learning techniques for two novel object-level saliency tasks; Deep neural network model pre-training with synthetic data; Thorough deep model analysis including useful visualization techniques and generalization tests; Fully reproducible with code, models and datasets available. For researchers interested in the intersection between digital topological theories and computer vision problems: Summary of theoretic findings and analysis of Boolean map distance; Theoretic algorithmic analysis; Applications in salient object detection and eye fixation prediction. Students majoring in image processing, machine learning and computer vision: This book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing; Some easy-to-implement algorithms for course projects with data provided (as links in the book); Hands-on programming exercises in digital topology and deep learning.

Download or read book Saliency Detection Using Horizontal and Vertical Color Differences written by 楊浩銘 and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Hierarchical Architectures for Saliency Prediction and Salient Object Detection written by Yu Hu and published by . This book was released on 2016 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the second investigation, I propose a hybrid Salient Object Detection (SOD) model that consists of the modified ASM and the potential Region-Of-Interest (p-ROI) approximation. Different from the ASM used in first investigation in which the ground truth of continuous saliency values is required to train the model, the ASM used in this investigation needs the binary ground truth only to detect salient objects. Specifically, the ASM aims to assign pixels in the input image with saliency values and p-ROI is used to validate the saliency region with a segmentation approach. Both ASM and PROI contribute to the improvement of object detection performance. ASM is used to refine performance of p-ROI by targeting at details, while p-ROI is to enhance the capability of ASM by exploring on the entire input image. The metrics including precision and recall curve and Area Under Curve (AUC) are adopted to evaluate the performance of my approach of SOD. Experimental results on a dataset with manually demarcated ground truth demonstrate a superior performance of the hybrid SOD model comparing with each individual method. In the third investigation, ASM is utilized to learn the heat maps of human eye gaze data. I first employ ASM with the Rprop algorithm to generate heat maps and show that the deep learning method can only achieve a moderate performance. Then I modify the approach to have the deep neural network pre-trained on Itti saliency maps and show that this pre-training process can slightly improve the performance. The metrics including precision and recall curve, Receiver Operating Characteristic (ROC) and AUC are adopted to evaluate the performance of my leaning model on both the OSIE dataset and the CAT2000 dataset.

Download or read book Visual Saliency Computation written by Jia Li and published by Springer. This book was released on 2014-04-12 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning. The book is also well-structured to address a wide range of readers, from specialists in the field to general readers interested in computer science and cognitive psychology. With this book, a reader can start from the very basic question of "what is visual saliency?" and progressively explore the problems in detecting salient locations, extracting salient objects, learning prior knowledge, evaluating performance, and using saliency in real-world applications. It is highly expected that this book will spark a great interest of research in the related communities in years to come.

Download or read book Effective Deep Learning Methodologies for Salient Object Detection written by Guangyu Ren and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Digital Image Processing Latest Advances and Applications written by Francisco Cuevas and published by BoD – Books on Demand. This book was released on 2024-07-24 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive analysis of image processing and its many applications in various fields. From improving the resolution of blurry images to identifying crop pests, optimizing water resource management, and extracting crucial details from photographs and videos, it covers a wide range of techniques and uses. Readers will be immersed in the fascinating world of image edge detection, combining color-based multidimensional scaling maps to highlight areas of saliency, and using deep learning to transform perception in driver assistance systems and autonomous vehicles. Additionally, they will explore how visual recognition can predict crack trajectories, bionic color theory, and the creation of realistic simulations of radar images. A highlight of the book is its focus on the revolutionary application of image processing in dentistry, from making precise measurements to developing next-generation dental biometrics systems. With a detailed and broad overview, this book provides readers with the tools and knowledge necessary to unlock the potential hidden in images, opening up new possibilities and applications in fields ranging from agriculture and medicine to technology and science.

Download or read book Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques written by Rui Guo and published by . This book was released on 2016 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics. To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges. In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1=2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure. To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research. To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks.

Download or read book Detection of Salient Objects in Images Using Frequency Domain and Deep Convolutional Features written by Masoumeh Rezaei Abkenar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In image processing and computer vision tasks such as object of interest image segmentation, adaptive image compression, object based image retrieval, seam carving, and medical imaging, the cost of information storage and computational complexity is generally a great concern. Therefore, for these and other applications, identifying and focusing only on the parts of the image that are visually most informative is much desirable. These most informative parts or regions that also have more contrast with the rest of the image are called the salient regions of the image, and the process of identifying them is referred to as salient object detection. The main challenges in devising a salient object detection scheme are in extracting the image features that correctly differentiate the salient objects from the non-salient ones, and then utilizing them to detect the salient objects accurately. Several salient object detection methods have been developed in the literature using spatial domain image features. However, these methods generally cannot detect the salient objects uniformly or with clear boundaries between the salient and non-salient regions. This is due to the fact that in these methods, unnecessary frequency content of the image get retained or the useful ones from the original image get suppressed. Frequency domain features can address these limitations by providing a better representation of the image. Some salient object detection schemes have been developed based on the features extracted using the Fourier or Fourier like transforms. While these methods are more successful in detecting the entire salient object in images with small salient regions, in images with large salient regions these methods have a tendency to highlight the boundaries of the salient region rather than doing so for the entire salient region. This is due to the fact that in the Fourier transform of an image, the global contrast is more dominant than the local ones. Moreover, it is known that the Fourier transform cannot provide simultaneous spatial and frequency localization. It is known that multi-resolution feature extraction techniques can provide more accurate features for different image processing tasks, since features that might not get extracted at one resolution may be detected at another resolution. However, not much work has been done to employ multi-resolution feature extraction techniques for salient object detection. In view of this, the objective of this thesis is to develop schemes for image salient object detection using multi-resolution feature extraction techniques both in the frequency domain and the spatial domain. The first part of this thesis is concerned with developing salient object detection methods using multi-resolution frequency domain features. The wavelet transform has the ability of performing multi-resolution simultaneous spatial and frequency localized analysis, which makes it a better feature extraction tool compared to the Fourier or other Fourier like transforms. In this part of the thesis, first a salient object detection scheme is developed by extracting features from the high-pass coefficients of the wavelet decompositions of the three color channels of images, and devising a scheme for the weighted linear combination of the color channel features. Despite the advantages of the wavelet transform in image feature extraction, it is not very effective in capturing line discontinuities, which correspond to directional information in the image. In order to circumvent the lack of directional flexibility of the wavelet-based features, in this part of the thesis, another salient object detection scheme is also presented by extracting local and global features from the non-subsampled contourlet coefficients of the image color channels. The local features are extracted from the local variations of the low-pass coefficients, whereas the global features are obtained based on the distribution of the subband coefficients afforded by the directional flexibility provided by the non-subsampled contourlet transform. In the past few years, there has been a surge of interest in employing deep convolutional neural networks to extract image features for different applications. These networks provide a platform for automatically extracting low-level appearance features and high-level semantic features at different resolutions from the raw images. The second part of this thesis is, therefore, concerned with the investigation of salient object detection using multiresolution deep convolutional features. The existing deep salient object detection schemes are based on the standard convolution. However, performing the standard convolution is computationally expensive specially when the number of channels increases through the layers of a deep network. In this part of the thesis, using a lightweight depthwise separable convolution, a deep salient object detection network that exploits the fusion of multi-level and multi-resolution image features through judicious skip connections between the layers is developed. The proposed deep salient object detection network is aimed at providing good performance with a much reduced complexity compared to the existing deep salient object detection methods. Extensive experiments are conducted in order to evaluate the performance of the proposed salient object detection methods by applying them to the natural images from several datasets. It is shown that the performance of the proposed methods are superior to that of the existing methods of salient object detection.

Download or read book Visual Saliency Prediction Based on Deep Learning written by Bashir Ghariba and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Human Visual System (HVS) has the ability to focus on specific parts of a scene, rather than the whole image. Human eye movement is also one of the primary functions used in our daily lives that helps us understand our surroundings. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. The outcomes that have been achieved by neural network methods in a variety of tasks have highlighted their ability to predict visual saliency. In particular, deep learning models have been used for visual saliency prediction. In this thesis, a deep learning method based on a transfer learning strategy is proposed (Chapter 2), wherein visual features in the convolutional layers are extracted from raw images to predict visual saliency (e.g., saliency map). Specifically, the proposed model uses the VGG-16 network (i.e., Pre-trained CNN model) for semantic segmentation. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are then quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. In Chapter 3, I specifically investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. Five deep learning models were trained over the SALICON dataset and used to predict visual saliency maps using four standard datasets, namely TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance. In Chapter 4, a novel deep learning model based on a Fully Convolutional Network (FCN) architecture is proposed. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The model is based on the encoder-decoder structure and includes two types of modules. The first has three stages of inception modules to improve multi-scale derivation and enhance contextual information. The second module includes one stage of the residual module to provide a more accurate recovery of information and to simplify optimization. The entire proposed model is fully trained from scratch to extract distinguishing features and to use a data augmentation technique to create variations in the images. The proposed model is evaluated using several benchmark datasets, including MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency. In Chapter 5, I study the possibility of using deep learning techniques for Salient Object Detection (SOD) because this work is slightly related to the problem of Visual saliency prediction. Therefore, in this work, the capability of ten well-known pre-trained models for semantic segmentation, including FCNs, VGGs, ResNets, MobileNet-v2, Xception, and InceptionResNet-v2, are investigated. These models have been trained over an ImageNet dataset, fine-tuned on a MSRA-10K dataset, and evaluated using other public datasets, such as ECSSD, MSRA-B, DUTS, and THUR15k. The results illustrate the superiority of ResNet50 and ResNet18, which have Mean Absolute Errors (MAE) of approximately 0.93 and 0.92, respectively, compared to other well-known FCN models. Finally, conclusions are drawn, and possible future works are discussed in chapter 6.

Download or read book Discovering Visual Saliency for Image Analysis written by Jongpil Kim and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Salient object detection is a key step in many image analysis tasks such as object detection and image segmentation, as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. Traditional methods of salient object detection are based on binary classification to determine whether a given pixel or region belongs to a salient object. However, binary classification-based approaches are limited because they ignore the shape of the salient object by assigning a single output value to an input (pixel, patch, or superpixel). In this work, we introduce novel salient object detection methods that consider the shape of the object. We claim that encoding spatial image content to facilitate the information of the object shape can result in more-accurate prediction of the salient object than the traditional binary classification-based approaches. We propose two deep learning-based salient object detection methods to detect the object. The first proposed method combines a shape-preserving saliency prediction driven by a convolutional neural network (CNN) with pre-defined saliency shapes. Our model learns a saliency shape dictionary, which is subsequently used to train a CNN to predict the salient class of a target region and estimate the full, but coarse, saliency map of the target image. The map is then refined using image-specific, low- to mid-level information. In the second method, we explicitly predict the shape of the salient object using a specially designed CNN model. The proposed CNN model facilitates both global and local context of the image to produce better prediction than that obtained by considering only the local information. We train our models with pixel-wise annotated training data. Experimental results show that the proposed methods outperform previous state-of-the-art methods in salient object detection. Next, we propose novel methods to find characteristic landmarks and recognize ancient Roman imperial coins. The Roman coins play an important role in understanding the Roman Empire because they convey rich information about key historical events of the time. Moreover, as large amounts of coins are traded daily over the Internet, it becomes necessary to develop automatic coin recognition systems to prevent illegal trades. Because the coin images do not have the pixel-wise annotations, we use a weakly-supervised approach to discover the characteristic landmarks on the coin images instead of using the previously mentioned models. For this purpose, we first propose a spatial-appearance coin recognition system to visualize the contribution of the image regions on the Roman coins using Fisher vector representation. Next, we formulate an optimization task to discover class-specific salient coin regions using CNNs. Analysis of discovered salient regions confirms that they are largely consistent with human expert annotations. Experimental results show that the proposed methods can effectively recognize the ancient Roman coins as well as successfully identify landmarks in the coin images and in a general fine-grained classification problem. For this research, we have collected new Roman coin datasets in which all coin images are annotated.

Download or read book Optics and Machine Vision for Marine Observation written by Hong Song and published by Frontiers Media SA. This book was released on 2023-10-13 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Saliency Detection Using Detail Map and Hybrid Loss Function written by 黃煜堯 and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Saliency Detection and Feature Matching for Object Segmentation in Digital Images written by Mehmood Nawaz and published by . This book was released on 2020 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Data Mining and Applications written by Jianxin Li and published by Springer Nature. This book was released on 2019-11-16 with total page 894 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 15th International Conference on Advanced Data Mining and Applications, ADMA 2019, held in Dalian, China in November 2019. The 39 full papers presented together with 26 short papers and 2 demo papers were carefully reviewed and selected from 170 submissions. The papers were organized in topical sections named: Data Mining Foundations; Classification and Clustering Methods; Recommender Systems; Social Network and Social Media; Behavior Modeling and User Profiling; Text and Multimedia Mining; Spatial-Temporal Data; Medical and Healthcare Data/Decision Analytics; and Other Applications.

Download or read book Visual Saliency Analysis Prediction and Visualization written by Ali Majeed Mahdi and published by . This book was released on 2019 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the recent years, a huge success has been accomplished in prediction of human eye fixations. Several studies employed deep learning to achieve high accuracy of prediction of human eye fixations. These studies rely on pre-trained deep learning for object classification. They exploit deep learning either as a transfer-learning problem, or the weights of the pre-trained network as the initialization to learn a saliency model. The utilization of such pre-trained neural networks is due to the relatively small datasets of human fixations available to train a deep learning model. Another relatively less prioritized problem is amount of computation of such deep learning models requires expensive hardware. In this dissertation, two approaches are proposed to tackle abovementioned problems. The first approach, codenamed DeepFeat, incorporates the deep features of convolutional neural networks pre-trained for object and scene classifications. This approach is the first approach that uses deep features without further learning. Performance of the DeepFeat model is extensively evaluated over a variety of datasets using a variety of implementations. The second approach is a deep learning saliency model, codenamed ClassNet. Two main differences separate the ClassNet from other deep learning saliency models. The ClassNet model is the only deep learning saliency model that learns its weights from scratch. In addition, the ClassNet saliency model treats prediction of human fixation as a classification problem, while other deep learning saliency models treat the human fixation prediction as a regression problem or as a classification of a regression problem.